Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Identifying liver cancer cells using cascaded convolutional neural network and gray level co-occurrence matrix techniques

Domain:

healthcare

Record type:

paper
Creator:
Bellary Chiterki, AnilArun Kumar, GowdruDayananda, PrithvirajaNiranjan Chanabasappa, Kundur
Publisher:
Zenodo
Host:avatar

Liver cancer has a high mortality rate, especially in South Asia, East Asia, and Sub-Saharan Africa. Efforts to reduce these rates focus on detecting liver cancer at all stages. Early detection allows more treatment options, though symptoms may not always be apparent. The staging process evaluates tumor size, location, lymph node involvement, and spread to other organs. Our research used the CLD staging system, assessing tumor size (C), lymph nodes (L), and distant invasion (D). We applied a deep learning approach with a cascaded convolutional neural network (CNN) and gray level co-occurrence matrix (GLCM)-based texture features to distinguish benign from malignant tumors. The method validated with the cancer imaging archive (TCIA) dataset, demonstrating superior accuracy compared to existing techniques.

Visit

doi.org

Tasks

computer visionimage classification

Tags

Computed tomographyHepatocellular carcinomaMetastatic carcinomaConvolutional neural networkRegion of interestMachine learning

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similar

A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image DenoisingBreast Cancer Detection Using Convolutional Neural NetworksCharacters recognition using keys points and convolutional neural networkAmharic spoken digits recognition using convolutional neural networkVehicle Detection in Bhutan Using Convolutional Neural NetworkAmharic Character Recognition Using Deep Convolutional Neural Network

A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising

Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliabilit

Breast Cancer Detection Using Convolutional Neural Networks

Breast cancer is prevalent in Ethiopia that accounts 34% among women cancer patients. The diagnosis

Characters recognition using keys points and convolutional neural network

In this paper, the convolutional neural network (CNN) is used in order to design an efficie

Amharic spoken digits recognition using convolutional neural network

Abstract Spoken digits recognition (SDR) is a type of supervised automatic speech recognition, whic

Vehicle Detection in Bhutan Using Convolutional Neural Network

Manual vehicle entry at different checkpoints in Bhutan by police personnel creates traffic congesti

Amharic Character Recognition Using Deep Convolutional Neural Network